This package implements model-robust standardization for cluster-randomized trials (CRTs). Provides functions that standardize user-specified regression models to estimate marginal treatment effects. The targets include the cluster-average and individual-average treatment effects, with utilities for variance estimation and example simulation datasets. Methods are described in Li, Tong, Fang, Cheng, Kahan, and Wang (2025) <doi:10.1002/sim.70270>.

The Cauchy distribution is a special case of the t distribution when the degrees of freedom are equal to 1. The functions are related to the multivariate Cauchy distribution and include simulation, computation of the density, maximum likelihood estimation, contour plot of the bivariate Cauchy distribution, and discriminant analysis. References include: Nadarajah S. and Kotz S. (2008). "Estimation methods for the multivariate t distribution". Acta Applicandae Mathematicae, 102(1): 99--118. <doi:10.1007/s10440-008-9212-8>, and Kanti V. Mardia, John T. Kent and John M. Bibby (1979). "Multivariate analysis", ISBN:978-0124712522. Academic Press, London.

Analysis of musical scales (& modes, grooves, etc.) in the vein of Sherrill 2025 <doi:10.1215/00222909-11595194>. The initials MCT in the package title refer to the article's title: "Modal Color Theory." Offers support for conventional musical pitch class set theory as developed by Forte (1973, ISBN: 9780300016109) and David Lewin (1987, ISBN: 9780300034936), as well as for the continuous geometries of Callender, Quinn, & Tymoczko (2008) <doi:10.1126/science.1153021>. Identifies structural properties of scales and calculates derived values (sign vector, color number, brightness ratio, etc.). Creates plots such as "brightness graphs" which visualize these properties.

Makes a word cloud of text, sized by the frequency of the word, and colored either by user-specified colors or colored by the strength of the coefficient of that text derived from a regression model.

Estimates key quantities in causal mediation analysis - including average causal mediation effects (indirect effects), average direct effects, total effects, and proportions mediated - in the presence of multiple uncausally related mediators. Methods are described by Jérolon et al., (2021) <doi:10.1515/ijb-2019-0088> and extended to accommodate survival outcomes as described by Domingo-Relloso et al., (2024) <doi:10.1101/2024.02.16.24302923>.

Implementation of Multidimensional Top Scoring method for creativity assessment proposed in Boris Forthmann, Maciej Karwowski, Roger E. Beaty (2023) <doi:10.1037/aca0000571>.

Detection of multivariate outliers using robust estimates of location and scale. The Minimum Covariance Determinant (MCD) estimator is used to calculate robust estimates of the mean vector and covariance matrix. Outliers are determined based on robust Mahalanobis distances using either an unstructured covariance matrix, a principal components structured covariance matrix, or a factor analysis structured covariance matrix. Includes options for specifying the direction of interest for outlier detection for each variable.

This package provides a suite of utility functions providing functionality commonly needed for production level projects such as logging, error handling, cache management and date-time parsing. Functions for date-time parsing and formatting require that time zones be specified explicitly, avoiding a common source of error when working with environmental time series.

Used for general multiple mediation analysis. The analysis method is described in Yu and Li (2022) (ISBN: 9780367365479) "Statistical Methods for Mediation, Confounding and Moderation Analysis Using R and SAS", published by Chapman and Hall/CRC; and Yu et al.(2017) <DOI:10.1016/j.sste.2017.02.001> "Exploring racial disparity in obesity: a mediation analysis considering geo-coded environmental factors", published on Spatial and Spatio-temporal Epidemiology, 21, 13-23.

Allows the user to estimate transition probabilities for migratory animals between any two phases of the annual cycle, using a variety of different data types. Also quantifies the strength of migratory connectivity (MC), a standardized metric to quantify the extent to which populations co-occur between two phases of the annual cycle. Includes functions to estimate MC and the more traditional metric of migratory connectivity strength (Mantel correlation) incorporating uncertainty from multiple sources of sampling error. For cross-species comparisons, methods are provided to estimate differences in migratory connectivity strength, incorporating uncertainty. See Cohen et al. (2018) <doi:10.1111/2041-210X.12916>, Cohen et al. (2019) <doi:10.1111/ecog.03974>, Roberts et al. (2023) <doi:10.1002/eap.2788>, and Hostetler et al. (2025) <doi:10.1111/2041-210X.14467> for details on some of these methods.

Application of a test to rule out that trends detected in hydrological time series are explained exclusively by the randomness of the climate. Based on: Ricchetti, (2018) <https://repositorio.uchile.cl/handle/2250/168487>.

BEAST2 (<https://www.beast2.org>) is a widely used Bayesian phylogenetic tool, that uses DNA/RNA/protein data and many model priors to create a posterior of jointly estimated phylogenies and parameters. mcbette allows to do a Bayesian model comparison over some site and clock models, using babette (<https://github.com/ropensci/babette/>).

This package provides modules as an organizational unit for source code. Modules enforce to be more rigorous when defining dependencies and have a local search path. They can be used as a sub unit within packages or in scripts.

An implementation of metaheuristic algorithms for continuous optimization. Currently, the package contains the implementations of 21 algorithms, as follows: particle swarm optimization (Kennedy and Eberhart, 1995), ant lion optimizer (Mirjalili, 2015 <doi:10.1016/j.advengsoft.2015.01.010>), grey wolf optimizer (Mirjalili et al., 2014 <doi:10.1016/j.advengsoft.2013.12.007>), dragonfly algorithm (Mirjalili, 2015 <doi:10.1007/s00521-015-1920-1>), firefly algorithm (Yang, 2009 <doi:10.1007/978-3-642-04944-6_14>), genetic algorithm (Holland, 1992, ISBN:978-0262581110), grasshopper optimisation algorithm (Saremi et al., 2017 <doi:10.1016/j.advengsoft.2017.01.004>), harmony search algorithm (Mahdavi et al., 2007 <doi:10.1016/j.amc.2006.11.033>), moth flame optimizer (Mirjalili, 2015 <doi:10.1016/j.knosys.2015.07.006>, sine cosine algorithm (Mirjalili, 2016 <doi:10.1016/j.knosys.2015.12.022>), whale optimization algorithm (Mirjalili and Lewis, 2016 <doi:10.1016/j.advengsoft.2016.01.008>), clonal selection algorithm (Castro, 2002 <doi:10.1109/TEVC.2002.1011539>), differential evolution (Das & Suganthan, 2011), shuffled frog leaping (Eusuff, Landsey & Pasha, 2006), cat swarm optimization (Chu et al., 2006), artificial bee colony algorithm (Karaboga & Akay, 2009), krill-herd algorithm (Gandomi & Alavi, 2012), cuckoo search (Yang & Deb, 2009), bat algorithm (Yang, 2012), gravitational based search (Rashedi et al., 2009) and black hole optimization (Hatamlou, 2013).

Computes multiple correlation coefficient when the data matrix is given and tests its significance.

This package provides a function to perform bias diagnostics on linear mixed models fitted with lmer() from the lme4 package. Implements permutation tests for assessing the bias of fixed effects, as described in Karl and Zimmerman (2021) <doi:10.1016/j.jspi.2020.06.004>. Karl and Zimmerman (2020) <doi:10.17632/tmynggddfm.1> provide R code for implementing the test using mvglmmRank output. Development of this package was assisted by GPT o1-preview for code structure and documentation.

Generate interactive html reports that enable quick visual review of multiple related time series stored in a data frame. For static datasets, this can help to identify any temporal artefacts that may affect the validity of subsequent analyses. For live data feeds, regularly scheduled reports can help to pro-actively identify data feed problems or unexpected trends that may require action. The reports are self-contained and shareable without a web server.

Hierarchical workspace tree, code editing and backup, easy package prep, editing of packages while loaded, per-object lazy-loading, easy documentation, macro functions, and miscellaneous utilities. Needed by debug package.

This package provides a framework to factorise electromyography (EMG) data. Tools are provided for raw data pre-processing, non negative matrix factorisation, classification of factorised data and plotting of obtained outcomes. In particular, reading from ASCII files is supported, along with wide-used filtering approaches to process EMG data. All steps include one or more sensible defaults that aim at simplifying the workflow. Yet, all functions are largely tunable at need. Example data sets are included.

This package contains several functions for statistical data analysis; e.g. for sample size and power calculations, computation of confidence intervals and tests, and generation of similarity matrices.

Inspired by pattern matching and enum types in Rust and many functional programming languages, this package offers an updated version of the switch function called Match that accepts atomic values, functions, expressions, and enum variants. Conditions and return expressions are separated by -> and multiple conditions can be associated with the same return expression using |'. Match also includes support for fallthrough'. The package also replicates the Result and Option enums from Rust.

The main objective of this package is to support the definition of Moodle elements taking advantage of the power that R offers. In this first version, it allows the definition of quizzes to be included in the question bank.

Apply tests of multiple comparisons based on studentized midrange and range distributions. The tests are: Tukey Midrange ('TM test), Student-Newman-Keuls Midrange ('SNKM test), Means Grouping Midrange ('MGM test) and Means Grouping Range ('MGR test). The first two tests were published by Batista and Ferreira (2020) <doi:10.1590/1413-7054202044008020>. The last two were published by Batista and Ferreira (2023) <doi:10.28951/bjb.v41i4.640>.

This package provides a complete toolkit to process the Munich ChronoType Questionnaire (MCTQ) for its three versions (standard, micro, and shift). MCTQ is a quantitative and validated tool to assess chronotypes using peoples sleep behavior, originally presented by Till Roenneberg, Anna Wirz-Justice, and Martha Merrow (2003, <doi:10.1177/0748730402239679>).